Autism Spectrum Disorder Prediction Model

Project Overview

This project develops a machine learning solution for early detection of Autism Spectrum Disorder (ASD) using multiple classification algorithms. By leveraging advanced data preprocessing and machine learning techniques, the model aims to provide a reliable screening tool for potential ASD identification.

Key Features

• Multiple Machine Learning Models:

  • Logistic Regression
  • XGBoost Classifier
  • Support Vector Machine (SVM)

• Advanced Data Preprocessing:

  • Feature engineering
  • Age group categorization
  • Log transformation
  • Label encoding
  • Feature scaling

• Handling Class Imbalance:

  • Random Over-Sampling technique to balance dataset

• Interactive Streamlit Web Application:

  • User-friendly interface
  • Model selection
  • Real-time ASD prediction

Installation

1. Clone the repository

git clone https://github.com/yourusername/autism-prediction.git
cd autism-prediction

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

Usage

Training the Model

python train.py

Running Streamlit App

streamlit run app.py

Model Performance Metrics

• Logistic Regression:

  • Training AUC: ~0.85
  • Validation AUC: ~0.80

• XGBoost:

  • Training AUC: ~0.90
  • Validation AUC: ~0.85

• Support Vector Machine:

  • Training AUC: ~0.88
  • Validation AUC: ~0.82

Dataset

The dataset includes various features related to:

• Demographic information
• Behavioral scores
• Medical history

Preprocessing Techniques

• Log transformation of age
• Feature engineering
• Label encoding
• Standard scaling
• Handling class imbalance

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

[MIT]